# sadie

##### Spatial Analysis by Distance IndicEs (SADIE).

`sadie`

performs the SADIE procedure. It computes different indices and
probabilities based on the distance to regularity for the observed spatial
pattern and a specified number of random permutations of this pattern. Both
kind of clustering indices described by Perry et al. (1999) and Li et al.
(2012) can be computed.

##### Usage

`sadie(data, ...)`# S3 method for data.frame
sadie(data, index = c("Perry", "Li-Madden-Xu", "all"),
nperm = 100, seed = NULL, threads = 1, ..., method = "shortsimplex",
verbose = TRUE)

# S3 method for matrix
sadie(data, index = c("Perry", "Li-Madden-Xu", "all"),
nperm = 100, seed = NULL, threads = 1, ..., method = "shortsimplex",
verbose = TRUE)

# S3 method for count
sadie(data, index = c("Perry", "Li-Madden-Xu", "all"),
nperm = 100, seed = NULL, threads = 1, ..., method = "shortsimplex",
verbose = TRUE)

# S3 method for incidence
sadie(data, index = c("Perry", "Li-Madden-Xu", "all"),
nperm = 100, seed = NULL, threads = 1, ..., method = "shortsimplex",
verbose = TRUE)

##### Arguments

- data
A data frame or a matrix with only three columns: the two first ones must be the x and y coordinates of the sampling units, and the last one, the corresponding disease intensity observations. It can also be a

`count`

or an`incidence`

object.- ...
Additional arguments to be passed to other methods.

- index
The index to be calculated: "Perry", "Li-Madden-Xu" or "all". By default, only Perry's index is computed for each sampling unit.

- nperm
Number of random permutations to assess probabilities.

- seed
Fixed seed to be used for randomizations (only useful for checking purposes). Not fixed by default (= NULL).

- threads
Number of threads to perform the computations.

- method
Method for the transportation algorithm.

- verbose
Explain what is being done (TRUE by default).

##### Details

By convention in the SADIE procedure, clustering indices for a donor unit (outflow) and a receiver unit (inflow) are positive and negative in sign, respectively.

##### References

Perry JN. 1995. Spatial analysis by distance indices. Journal of Animal Ecology 64, 303<U+2013>314. doi:10.2307/5892

Perry JN, Winder L, Holland JM, Alston RD. 1999. Red<U+2013>blue plots for detecting clusters in count data. Ecology Letters 2, 106<U+2013>113. doi:10.1046/j.1461-0248.1999.22057.x

Li B, Madden LV, Xu X. 2012. Spatial analysis by distance indices: an alternative local clustering index for studying spatial patterns. Methods in Ecology and Evolution 3, 368<U+2013>377. doi:10.1111/j.2041-210X.2011.00165.x

##### Examples

```
# NOT RUN {
set.seed(123)
# Create an intensity object:
my_count <- count(aphids, mapping(x = xm, y = ym))
# Only compute Perry's indices:
my_res <- sadie(my_count)
my_res
summary(my_res)
plot(my_res)
plot(my_res, isoclines = TRUE)
set.seed(123)
# Compute both Perry's and Li-Madden-Xu's indices (using multithreading):
my_res <- sadie(my_count, index = "all", threads = 2, nperm = 20)
my_res
summary(my_res)
plot(my_res) # Identical to: plot(my_res, index = "Perry")
plot(my_res, index = "Li-Madden-Xu")
set.seed(123)
# Using usual data frames instead of intensity objects:
my_df <- aphids[, c("xm", "ym", "i")]
sadie(my_df)
# }
```

*Documentation reproduced from package epiphy, version 0.3.4, License: MIT + file LICENSE*